Synthetic and Encoded Database of Dengue, Zika, Chikungunya, and Influenza Derived from the Literature

This work presents a synthetic binary database of Dengue, Zika, Chikungunya, and Influenza constructed entirely from clinical information extracted from the scientific literature. Due to the limited availability and heterogeneity of clinical records in medical units—particularly for arboviral diseases—existing datasets are often insufficient for developing robust Machine Learning models. To address this limitation, an extensive search of PubMed and Google Scholar was conducted between February 2024 and May 2025, following strict selection criteria focused on diagnostic confirmation. The resulting dataset comprises 48,214 records and 67 standardized signs and symptoms, homogenized across all pathologies. Each record is fully binary, contains no missing values, and represents symptom presence or absence. The composition includes 22,379 Dengue records, 7135 Zika records, 7959 Chikungunya records, and 10,741 Influenza records. Symptom prevalence was analyzed, revealing consistency with patterns reported in epidemiological and clinical studies, supporting the dataset’s plausibility. This database enables statistical exploration and direct integration into Machine Learning pipelines without the need for imputation. It has been used in an in silico predictive study of arboviral diseases, employing Influenza as a negative control, and serves as a reproducible, literature-derived resource for computational modeling.